Imagine you’ve created a machine learning model and are eager to share it with others. Consider the following scenarios for sharing your model:
https://cpsc330-moment-predictor.onrender.com/
https://canvas.ubc.ca/courses/149122/external_tools/53187
Part 1: Supervised learning on tabular data: ML fundamentals, preprocessing and data encoding, a bunch of models, evaluation metrics, feature importances and model transparency, feature selection, hyperparameter optimization
Part 2: Dealing with other non-tabular data types: Clustering, recommender systems, computer vision with pre-trained deep learning models (high level), language data, text preprocessing, embeddings, topic modeling, time series, right-censored data / survival analysis
Part 3: Communication, Ethics, and Deployment
Lots of room for improvement. Here are some things on my mind.
- Flipped classroom in a more effective way in the first part of the course. - More demos during the lecture time - Worksheets/practice questions during tutorials - Course project?? - Add more interactive components in the lectures - Some material to cover: dealing with outliers, data collection, large language models
If you want to further develop your machine learning skills: - Practice! - Work on your own projects - Work hard and be consistent.
That’s all, folks. We made it! Good luck on your final exam! When you get a chance, please let me know what worked for you and what didn’t work for you in this course.